Emotions in Veterinary Surgical Students: A Qualitative Study
Bibliographic record
Abstract
A surgical educational environment is potentially stressful and can negatively affect students' learning. The aim of the present study was to investigate the emotions experienced by veterinary students in relation to their first encounter with live-animal surgery and to identify possible sources of positive and negative emotions, respectively. During a Basic Surgical Skills course, 155 veterinary fourth-year students completed a survey. Of these, 26 students additionally participated in individual semi-structured interviews. The results of the study show that students often experienced a combination of emotions; 63% of students experienced negative emotions, while 58% experienced positive ones. In addition, 61% of students reported feeling excited or tense. Students' statements reveal that anxiety is perceived as counterproductive to learning, while excitement seems to enhance students' focus and engagement. Our study identified the most common sources of positive and negative emotions to be "being able to prepare well" and "lack of self-confidence," respectively. Our findings suggest that there are factors that we can influence in the surgical learning environment to minimize negative emotions and enhance positive emotions and engagement, thereby improving students' learning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".